This report covers the survey about attitudes collected by Richard Childers, MD and Joel Schofer, MD.
year_executed_order value was missing.Warning: Factor `iv` contains implicit NA, consider using `forcats::fct_explicit_na`
Warning: Factor `iv` contains implicit NA, consider using `forcats::fct_explicit_na`
Warning: Factor `iv` contains implicit NA, consider using `forcats::fct_explicit_na`
ds ~ assignment_priority 1 + specialty_type
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 1.3588117 | 0.1230616 | 11.0417196 | 0.0000000 |
| specialty_typesurgical | 0.2241929 | 0.2599057 | 0.8625933 | 0.3883611 |
| specialty_typefamily | 0.6780702 | 0.2791850 | 2.4287484 | 0.0151510 |
| specialty_typeoperational | 0.6290626 | 0.3589444 | 1.7525349 | 0.0796819 |
| specialty_typeresident | -0.1060487 | 0.4193546 | -0.2528856 | 0.8003566 |
| null.deviance | df.null | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|
| 754.3152 | 815 | -372.6514 | 755.3028 | 778.8249 | 745.3028 | 811 |
ds ~ officer_rank_priority 1 + officer_rank
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 1.1526795 | 0.1655664 | 6.9620373 | 0.0000000 |
| officer_rankLCDR | -0.2043494 | 0.2086735 | -0.9792783 | 0.3274425 |
| officer_rankCDR | 0.4159364 | 0.2488695 | 1.6713030 | 0.0946618 |
| officer_rankCAPT or Flag | -0.3671590 | 0.2727199 | -1.3462860 | 0.1782103 |
| null.deviance | df.null | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|
| 905.4486 | 809 | -447.4729 | 902.9458 | 921.7339 | 894.9458 | 806 |
| satisfaction rank | transparency rank | favoritism rank | assignment current choice | |
|---|---|---|---|---|
| satisfaction_rank | 1.000 | 0.771 | 0.486 | -0.519 |
| transparency_rank | 0.771 | 1.000 | 0.488 | -0.405 |
| favoritism_rank | 0.486 | 0.488 | 1.000 | -0.325 |
| assignment_current_choice | -0.519 | -0.405 | -0.325 | 1.000 |
ds ~ satisfaction_rank 1 + officer_rate_f
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.1361502 | 0.0903269 | 34.720017 | 0.00e+00 |
| officer_rate_f4 | 0.4547589 | 0.1158670 | 3.924834 | 9.37e-05 |
| officer_rate_f5 | 0.7953566 | 0.1268635 | 6.269388 | 0.00e+00 |
| officer_rate_f6 | 0.9934794 | 0.1557247 | 6.379715 | 0.00e+00 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0619066 | 0.0586569 | 1.318277 | 19.04967 | 0 | 4 | -1472.875 | 2955.751 | 2979.593 | 1504.982 | 866 |
ds ~ transparency_rank 1 + officer_rate_f
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.9018692 | 0.0923961 | 31.406827 | 0e+00 |
| officer_rate_f4 | 0.5921428 | 0.1183507 | 5.003290 | 7e-07 |
| officer_rate_f5 | 0.8972176 | 0.1299199 | 6.905929 | 0e+00 |
| officer_rate_f6 | 1.0425753 | 0.1595401 | 6.534880 | 0e+00 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0685919 | 0.0653839 | 1.351639 | 21.38109 | 0 | 4 | -1503.22 | 3016.439 | 3040.31 | 1591.254 | 871 |
ds ~ favoritism_rank 1 + officer_rate_f
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.1216216 | 0.1092427 | 28.575103 | 0.0000000 |
| officer_rate_f4 | 0.2028817 | 0.1333507 | 1.521415 | 0.1285723 |
| officer_rate_f5 | 0.2581861 | 0.1429176 | 1.806538 | 0.0712303 |
| officer_rate_f6 | 0.2974260 | 0.1695735 | 1.753965 | 0.0798401 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.005615 | 0.0016846 | 1.328995 | 1.428604 | 0.2331066 | 4 | -1297.66 | 2605.319 | 2628.505 | 1340.567 | 759 |
ds ~ assignment_current_choice 1 + officer_rate_f
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 1.8177083 | 0.0808428 | 22.484485 | 0.0000000 |
| officer_rate_f4 | -0.1670234 | 0.1040813 | -1.604740 | 0.1089568 |
| officer_rate_f5 | -0.4257485 | 0.1133191 | -3.757077 | 0.0001848 |
| officer_rate_f6 | -0.4136679 | 0.1386022 | -2.984570 | 0.0029287 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0222757 | 0.0185056 | 1.120191 | 5.908457 | 0.0005478 | 4 | -1196.361 | 2402.722 | 2426.031 | 976.2552 | 778 |
[ ds ds$specialty_type != “unknown” ~ satisfaction_rank 1 + specialty_type
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.9172414 | 0.0625729 | 62.602823 | 0.0000000 |
| specialty_typesurgical | -0.2326776 | 0.1238796 | -1.878257 | 0.0606834 |
| specialty_typefamily | -0.5635828 | 0.1195853 | -4.712809 | 0.0000028 |
| specialty_typeoperational | -1.2544507 | 0.1540125 | -8.145121 | 0.0000000 |
| specialty_typeresident | -0.4020899 | 0.2356418 | -1.706360 | 0.0883012 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0811978 | 0.0769342 | 1.305062 | 19.04449 | 0 | 5 | -1458.551 | 2929.103 | 2957.693 | 1468.146 | 862 |
[ ds ds$specialty_type != “unknown” ~ transparency_rank 1 + specialty_type
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.7214612 | 0.0646380 | 57.5738850 | 0.0000000 |
| specialty_typesurgical | -0.0888081 | 0.1289458 | -0.6887244 | 0.4911810 |
| specialty_typefamily | -0.4850976 | 0.1235676 | -3.9257663 | 0.0000933 |
| specialty_typeoperational | -1.1051821 | 0.1595528 | -6.9267494 | 0.0000000 |
| specialty_typeresident | -0.5825723 | 0.2345448 | -2.4838419 | 0.0131853 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0632343 | 0.0589124 | 1.352773 | 14.63123 | 0 | 5 | -1498.288 | 3008.576 | 3037.201 | 1586.606 | 867 |
[ ds ds$specialty_type != “unknown” ~ favoritism_rank 1 + specialty_type
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.4692308 | 0.0662709 | 52.3492083 | 0.0000000 |
| specialty_typesurgical | -0.1358974 | 0.1296285 | -1.0483608 | 0.2948074 |
| specialty_typefamily | -0.3889388 | 0.1299775 | -2.9923554 | 0.0028584 |
| specialty_typeoperational | -0.7633484 | 0.1719893 | -4.4383491 | 0.0000104 |
| specialty_typeresident | 0.1021978 | 0.2560544 | 0.3991254 | 0.6899135 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0328075 | 0.0276901 | 1.308746 | 6.410944 | 4.48e-05 | 5 | -1282.066 | 2576.132 | 2603.939 | 1294.889 | 756 |
[ ds ds$specialty_type != “unknown” ~ assignment_current_choice 1 + specialty_type
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 1.4912281 | 0.0561207 | 26.5718057 | 0.0000000 |
| specialty_typesurgical | 0.3361101 | 0.1104096 | 3.0442111 | 0.0024118 |
| specialty_typefamily | 0.0957285 | 0.1107059 | 0.8647099 | 0.3874656 |
| specialty_typeoperational | 0.4933873 | 0.1499427 | 3.2905058 | 0.0010453 |
| specialty_typeresident | -0.2091768 | 0.1880736 | -1.1122073 | 0.2663939 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0255001 | 0.0204704 | 1.12101 | 5.069921 | 0.0004894 | 5 | -1193.363 | 2398.727 | 2426.683 | 973.914 | 775 |
ds ~ satisfaction_rank 1 + bonus_pay_cut4
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.8411215 | 0.1285699 | 22.097867 | 0e+00 |
| bonus_pay_cut4$20-24k | 0.8846169 | 0.1423437 | 6.214653 | 0e+00 |
| bonus_pay_cut4$24-32k | 0.9289934 | 0.1633872 | 5.685841 | 0e+00 |
| bonus_pay_cut432k+ | 0.9110153 | 0.1778978 | 5.121004 | 4e-07 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0472242 | 0.0439312 | 1.329938 | 14.34077 | 0 | 4 | -1483.945 | 2977.89 | 3001.744 | 1535.262 | 868 |
ds ~ transparency_rank 1 + bonus_pay_cut4
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.6363636 | 0.1296906 | 20.328104 | 0e+00 |
| bonus_pay_cut4$20-24k | 0.9138456 | 0.1438411 | 6.353158 | 0e+00 |
| bonus_pay_cut4$24-32k | 1.1324225 | 0.1658741 | 6.827000 | 0e+00 |
| bonus_pay_cut432k+ | 0.9153605 | 0.1810230 | 5.056599 | 5e-07 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0559187 | 0.0526745 | 1.360206 | 17.23618 | 0 | 4 | -1512.202 | 3034.403 | 3058.286 | 1615.191 | 873 |
ds ~ favoritism_rank 1 + bonus_pay_cut4
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.0119048 | 0.1448614 | 20.791630 | 0.0000000 |
| bonus_pay_cut4$20-24k | 0.3295587 | 0.1590099 | 2.072567 | 0.0385488 |
| bonus_pay_cut4$24-32k | 0.4111722 | 0.1796785 | 2.288378 | 0.0223888 |
| bonus_pay_cut432k+ | 0.2489648 | 0.1905594 | 1.306495 | 0.1917791 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0075041 | 0.0035915 | 1.327677 | 1.917942 | 0.1251881 | 4 | -1300.307 | 2610.614 | 2633.814 | 1341.434 | 761 |
ds ~ assignment_current_choice 1 + bonus_pay_cut4
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 1.7978723 | 0.1164006 | 15.445565 | 0.0000000 |
| bonus_pay_cut4$20-24k | -0.2789150 | 0.1287134 | -2.166946 | 0.0305410 |
| bonus_pay_cut4$24-32k | -0.1312057 | 0.1468307 | -0.893585 | 0.3718196 |
| bonus_pay_cut432k+ | -0.1740191 | 0.1588510 | -1.095487 | 0.2736415 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0072089 | 0.0033904 | 1.128545 | 1.887914 | 0.1301122 | 4 | -1205.251 | 2420.503 | 2443.825 | 993.4192 | 780 |
ds ~ satisfaction_rank 1 + assignment_current_choice
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 4.7192430 | 0.0674241 | 69.99342 | 0 |
| assignment_current_choice | -0.5721764 | 0.0342102 | -16.72533 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.2693047 | 0.268342 | 1.074932 | 279.7366 | 0 | 2 | -1133.799 | 2273.598 | 2287.502 | 877.0091 | 759 |
ds ~ transparency_rank 1 + assignment_current_choice
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 4.3945478 | 0.0758513 | 57.93639 | 0 |
| assignment_current_choice | -0.4738032 | 0.0387336 | -12.23236 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.1643153 | 0.1632172 | 1.209629 | 149.6306 | 0 | 2 | -1226.858 | 2459.717 | 2473.629 | 1113.498 | 761 |
ds ~ favoritism_rank 1 + assignment_current_choice
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.9860828 | 0.0822266 | 48.476775 | 0 |
| assignment_current_choice | -0.3706206 | 0.0416331 | -8.902061 | 0 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.1053467 | 0.1040173 | 1.232284 | 79.24669 | 0 | 2 | -1097.769 | 2201.538 | 2215.082 | 1021.967 | 673 |
ds ~ satisfaction_rank 1 + billet_current
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.8472469 | 0.0553450 | 69.5139096 | 0.0000000 |
| billet_currentNon-Operational/Clinical | -0.2014136 | 0.1974596 | -1.0200240 | 0.3080020 |
| billet_currentOCONUS MTF | -0.4372469 | 0.1425066 | -3.0682562 | 0.0022201 |
| billet_currentCONUS Operational | -0.7311755 | 0.1358693 | -5.3814634 | 0.0000001 |
| billet_currentOCONUS Operational | -1.3972469 | 0.2148854 | -6.5022872 | 0.0000000 |
| billet_currentOther | 0.1527531 | 0.4412199 | 0.3462063 | 0.7292718 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0731888 | 0.0678376 | 1.313205 | 13.67732 | 0 | 6 | -1471.899 | 2957.797 | 2991.193 | 1493.423 | 866 |
ds ~ satisfaction_rank 1 + geographic_preference
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.4523810 | 0.1206561 | 28.6133918 | 0.0000000 |
| geographic_preferenceMiddle East or Africa | -0.8523810 | 0.6175897 | -1.3801736 | 0.1678910 |
| geographic_preferenceNational Capital Region | 0.2765910 | 0.1780473 | 1.5534691 | 0.1206782 |
| geographic_preferenceNortheast | -0.2349896 | 0.3070991 | -0.7651915 | 0.4443670 |
| geographic_preferencePacific (Hawaii, Guam, Japan) | 0.2800134 | 0.2009802 | 1.3932387 | 0.1639068 |
| geographic_preferencePacific Northwest | -0.0157612 | 0.2009802 | -0.0784218 | 0.9375107 |
| geographic_preferenceSoutheast (North Carolina, Florida) | 0.1309524 | 0.1834806 | 0.7137123 | 0.4755983 |
| geographic_preferenceSouthern California | 0.2957393 | 0.1464710 | 2.0190986 | 0.0437861 |
| geographic_preferenceTidewater Region (Portsmouth, Norfolk) | 0.3637110 | 0.1887902 | 1.9265350 | 0.0543661 |
| geographic_preferenceUnknown | -0.3523810 | 0.3259949 | -1.0809401 | 0.2800261 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0187382 | 0.008493 | 1.354362 | 1.828979 | 0.0595147 | 10 | -1496.789 | 3015.579 | 3068.058 | 1581.163 | 862 |
[ ds ds$specialty_type != “unknown” ~ satisfaction_rank 1 + officer_rate_f * specialty_type
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.7794118 | 0.1553781 | 24.3239628 | 0.0000000 |
| officer_rate_f4 | -0.0092968 | 0.1832411 | -0.0507355 | 0.9595483 |
| officer_rate_f5 | 0.2560492 | 0.1891705 | 1.3535366 | 0.1762449 |
| officer_rate_f6 | 0.5539216 | 0.2373440 | 2.3338342 | 0.0198372 |
| specialty_typesurgical | -0.1127451 | 0.3654966 | -0.3084710 | 0.7577997 |
| specialty_typefamily | -0.9315857 | 0.2446039 | -3.8085478 | 0.0001499 |
| specialty_typeoperational | -1.3667134 | 0.2240553 | -6.0998925 | 0.0000000 |
| specialty_typeresident | -0.2294118 | 0.3259239 | -0.7038814 | 0.4816999 |
| officer_rate_f4:specialty_typesurgical | -0.2384509 | 0.4064573 | -0.5866567 | 0.5575904 |
| officer_rate_f5:specialty_typesurgical | 0.0516431 | 0.4328109 | 0.1193202 | 0.9050499 |
| officer_rate_f6:specialty_typesurgical | -0.1253501 | 0.4939158 | -0.2537885 | 0.7997205 |
| officer_rate_f4:specialty_typefamily | 0.6244337 | 0.3157016 | 1.9779237 | 0.0482607 |
| officer_rate_f5:specialty_typefamily | 0.3285571 | 0.3403586 | 0.9653265 | 0.3346565 |
| officer_rate_f6:specialty_typefamily | 0.4871412 | 0.3909269 | 1.2461183 | 0.2130651 |
| officer_rate_f4:specialty_typeoperational | 0.6680270 | 0.4205932 | 1.5882972 | 0.1125919 |
| officer_rate_f5:specialty_typeoperational | 2.3312524 | 1.3051914 | 1.7861383 | 0.0744341 |
| officer_rate_f6:specialty_typeoperational | 0.6583800 | 0.5362841 | 1.2276701 | 0.2199115 |
| officer_rate_f4:specialty_typeresident | -0.0791647 | 0.4918785 | -0.1609436 | 0.8721761 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.1249664 | 0.1074244 | 1.281281 | 7.123859 | 0 | 18 | -1434.353 | 2906.706 | 2997.22 | 1392.145 | 848 |
[ ds ds$specialty_type != “unknown” ~ satisfaction_rank 1 + officer_rate_f + specialty_type
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.6060419 | 0.1138533 | 31.6726901 | 0.0000000 |
| officer_rate_f4 | 0.1894040 | 0.1227398 | 1.5431344 | 0.1231669 |
| officer_rate_f5 | 0.4624532 | 0.1364136 | 3.3900819 | 0.0007306 |
| officer_rate_f6 | 0.7806986 | 0.1587342 | 4.9182747 | 0.0000010 |
| specialty_typesurgical | -0.2466207 | 0.1222314 | -2.0176535 | 0.0439382 |
| specialty_typefamily | -0.5476117 | 0.1188297 | -4.6083747 | 0.0000047 |
| specialty_typeoperational | -1.0520849 | 0.1656575 | -6.3509630 | 0.0000000 |
| specialty_typeresident | -0.1655041 | 0.2383040 | -0.6945081 | 0.4875516 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.113058 | 0.1058219 | 1.282431 | 15.62411 | 0 | 8 | -1440.206 | 2898.412 | 2941.287 | 1411.091 | 858 |
ds ~ transparency_rank 1 + specialty_type
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.7214612 | 0.0648138 | 57.4177578 | 0.0000000 |
| specialty_typesurgical | -0.0888081 | 0.1292964 | -0.6868568 | 0.4923558 |
| specialty_typefamily | -0.4850976 | 0.1239036 | -3.9151205 | 0.0000974 |
| specialty_typeoperational | -1.1051821 | 0.1599866 | -6.9079656 | 0.0000000 |
| specialty_typeresident | -0.5825723 | 0.2351826 | -2.4771063 | 0.0134340 |
| specialty_typeunknown | -0.7214612 | 0.6100763 | -1.1825754 | 0.2373002 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0632744 | 0.0578971 | 1.356452 | 11.76695 | 0 | 6 | -1508.772 | 3031.543 | 3064.979 | 1602.606 | 871 |
ds ~ favoritism_rank 1 + specialty_type
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.4692308 | 0.0664647 | 52.1966278 | 0.0000000 |
| specialty_typesurgical | -0.1358974 | 0.1300074 | -1.0453052 | 0.2962147 |
| specialty_typefamily | -0.3889388 | 0.1303574 | -2.9836337 | 0.0029398 |
| specialty_typeoperational | -0.7633484 | 0.1724920 | -4.4254128 | 0.0000110 |
| specialty_typeresident | 0.1021978 | 0.2568029 | 0.3979621 | 0.6907700 |
| specialty_typeunknown | -0.2192308 | 0.6596428 | -0.3323477 | 0.7397185 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0325083 | 0.0261348 | 1.312572 | 5.100571 | 0.0001313 | 6 | -1290.547 | 2595.095 | 2627.574 | 1307.639 | 759 |
ds ~ assignment_current_choice 1 + specialty_type
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 1.4912281 | 0.0560339 | 26.6129401 | 0.0000000 |
| specialty_typesurgical | 0.3361101 | 0.1102389 | 3.0489237 | 0.0023744 |
| specialty_typefamily | 0.0957285 | 0.1105347 | 0.8660485 | 0.3867305 |
| specialty_typeoperational | 0.4933873 | 0.1497109 | 3.2955997 | 0.0010266 |
| specialty_typeresident | -0.2091768 | 0.1877829 | -1.1139290 | 0.2656536 |
| specialty_typeunknown | -0.2412281 | 0.5624368 | -0.4288981 | 0.6681161 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0259522 | 0.0196923 | 1.119277 | 4.145762 | 0.0010079 | 6 | -1197.78 | 2409.56 | 2442.211 | 974.664 | 778 |
ds ~ satisfaction_rank 1 + officer_rate_f + assignment_current_choice
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 4.2860493 | 0.0996705 | 43.002177 | 0.0000000 |
| officer_rate_f4 | 0.3900905 | 0.0997785 | 3.909563 | 0.0001008 |
| officer_rate_f5 | 0.5345137 | 0.1088637 | 4.909937 | 0.0000011 |
| officer_rate_f6 | 0.7636977 | 0.1325867 | 5.759988 | 0.0000000 |
| assignment_current_choice | -0.5388720 | 0.0338151 | -15.935829 | 0.0000000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.3052065 | 0.3015205 | 1.048202 | 82.80361 | 0 | 5 | -1110.197 | 2232.395 | 2260.187 | 828.4411 | 754 |
ds ~ satisfaction_rank 1 + officer_rate_f * assignment_current_choice
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 4.2547092 | 0.1334327 | 31.8865648 | 0.0000000 |
| officer_rate_f4 | 0.4158921 | 0.1724797 | 2.4112525 | 0.0161369 |
| officer_rate_f5 | 0.6520120 | 0.1918718 | 3.3981645 | 0.0007142 |
| officer_rate_f6 | 0.7511815 | 0.2254399 | 3.3320701 | 0.0009042 |
| assignment_current_choice | -0.5218961 | 0.0587130 | -8.8889425 | 0.0000000 |
| officer_rate_f4:assignment_current_choice | -0.0136552 | 0.0796330 | -0.1714763 | 0.8638954 |
| officer_rate_f5:assignment_current_choice | -0.0789217 | 0.1018947 | -0.7745421 | 0.4388541 |
| officer_rate_f6:assignment_current_choice | 0.0140756 | 0.1196004 | 0.1176887 | 0.9063458 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.3058807 | 0.2994109 | 1.049784 | 47.27809 | 0 | 8 | -1109.829 | 2237.658 | 2279.346 | 827.6371 | 751 |
Analysis of Variance Table
Model 1: satisfaction_rank ~ 1 + officer_rate_f + assignment_current_choice Model 2: satisfaction_rank ~ 1 + officer_rate_f * assignment_current_choice Res.Df RSS Df Sum of Sq F Pr(>F) 1 754 828.44
2 751 827.64 3 0.80396 0.2432 0.8662 ### transparency_rank
ds ~ transparency_rank 1 + officer_rate_f * assignment_current_choice
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.8638632 | 0.1499297 | 25.7711726 | 0.0000000 |
| officer_rate_f4 | 0.4825983 | 0.1937144 | 2.4912871 | 0.0129424 |
| officer_rate_f5 | 0.6305539 | 0.2161270 | 2.9175158 | 0.0036333 |
| officer_rate_f6 | 1.0465560 | 0.2579506 | 4.0571960 | 0.0000548 |
| assignment_current_choice | -0.4305167 | 0.0660775 | -6.5153323 | 0.0000000 |
| officer_rate_f4:assignment_current_choice | 0.0217793 | 0.0896179 | 0.2430244 | 0.8080527 |
| officer_rate_f5:assignment_current_choice | 0.0254252 | 0.1147065 | 0.2216542 | 0.8246432 |
| officer_rate_f6:assignment_current_choice | -0.2130761 | 0.1435839 | -1.4839828 | 0.1382319 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.2076297 | 0.2002637 | 1.18273 | 28.1876 | 0 | 8 | -1203.506 | 2425.012 | 2466.724 | 1053.334 | 753 |
ds ~ favoritism_rank 1 + officer_rate_f * assignment_current_choice
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.8687050 | 0.1889326 | 20.4766424 | 0.0000000 |
| officer_rate_f4 | 0.1174241 | 0.2324391 | 0.5051821 | 0.6135983 |
| officer_rate_f5 | 0.0937283 | 0.2502167 | 0.3745885 | 0.7080859 |
| officer_rate_f6 | 0.3797836 | 0.2860813 | 1.3275372 | 0.1847866 |
| assignment_current_choice | -0.3171891 | 0.0798386 | -3.9728796 | 0.0000788 |
| officer_rate_f4:assignment_current_choice | -0.0355036 | 0.1036411 | -0.3425626 | 0.7320358 |
| officer_rate_f5:assignment_current_choice | -0.0549110 | 0.1273840 | -0.4310667 | 0.6665595 |
| officer_rate_f6:assignment_current_choice | -0.2321125 | 0.1465992 | -1.5833140 | 0.1138253 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.108222 | 0.0988349 | 1.235585 | 11.52876 | 0 | 8 | -1093.291 | 2204.582 | 2245.188 | 1015.236 | 665 |
ds ~ satisfaction_rank 1 + officer_rate_f + bonus_pay
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.9244122 | 0.1132714 | 25.817747 | 0.0000000 |
| officer_rate_f4 | 0.2934115 | 0.1267040 | 2.315724 | 0.0208064 |
| officer_rate_f5 | 0.6032909 | 0.1408814 | 4.282261 | 0.0000206 |
| officer_rate_f6 | 0.8478220 | 0.1620620 | 5.231467 | 0.0000002 |
| bonus_pay | 0.0000158 | 0.0000051 | 3.072155 | 0.0021917 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0720318 | 0.0677406 | 1.311901 | 16.786 | 0 | 5 | -1468.155 | 2948.309 | 2976.92 | 1488.738 | 865 |
ds ~ satisfaction_rank 1 + officer_rate_f * bonus_pay
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.6738295 | 0.1412220 | 18.933520 | 0.0000000 |
| officer_rate_f4 | 0.9876528 | 0.2498733 | 3.952615 | 0.0000836 |
| officer_rate_f5 | 0.8154021 | 0.3806246 | 2.142274 | 0.0324510 |
| officer_rate_f6 | 1.1783927 | 0.4842708 | 2.433334 | 0.0151628 |
| bonus_pay | 0.0000345 | 0.0000081 | 4.232110 | 0.0000256 |
| officer_rate_f4:bonus_pay | -0.0000374 | 0.0000115 | -3.245845 | 0.0012162 |
| officer_rate_f5:bonus_pay | -0.0000172 | 0.0000157 | -1.096670 | 0.2730919 |
| officer_rate_f6:bonus_pay | -0.0000222 | 0.0000213 | -1.042449 | 0.2974956 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0832837 | 0.0758394 | 1.30619 | 11.18754 | 0 | 8 | -1462.848 | 2943.696 | 2986.612 | 1470.687 | 862 |
Analysis of Variance Table
Model 1: satisfaction_rank ~ 1 + officer_rate_f + bonus_pay Model 2: satisfaction_rank ~ 1 + officer_rate_f * bonus_pay Res.Df RSS Df Sum of Sq F Pr(>F) 1 865 1488.7
2 862 1470.7 3 18.052 3.5268 0.01461 ### transparency_rank
ds ~ transparency_rank 1 + officer_rate_f * bonus_pay
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.4796068 | 0.1432849 | 17.3054341 | 0.0000000 |
| officer_rate_f4 | 1.0075219 | 0.2531992 | 3.9791673 | 0.0000749 |
| officer_rate_f5 | 0.8584585 | 0.3901640 | 2.2002504 | 0.0280522 |
| officer_rate_f6 | 0.8102031 | 0.5006221 | 1.6183927 | 0.1059417 |
| bonus_pay | 0.0000318 | 0.0000083 | 3.8335875 | 0.0001354 |
| officer_rate_f4:bonus_pay | -0.0000315 | 0.0000117 | -2.6878622 | 0.0073290 |
| officer_rate_f5:bonus_pay | -0.0000138 | 0.0000161 | -0.8554228 | 0.3925535 |
| officer_rate_f6:bonus_pay | -0.0000028 | 0.0000221 | -0.1257225 | 0.8999807 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0879753 | 0.0806117 | 1.340582 | 11.94744 | 0 | 8 | -1494.019 | 3006.038 | 3049.006 | 1558.138 | 867 |
ds ~ favoritism_rank 1 + officer_rate_f * bonus_pay
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.8463186 | 0.1678880 | 16.9536708 | 0.0000000 |
| officer_rate_f4 | 0.6600801 | 0.2736657 | 2.4119942 | 0.0161031 |
| officer_rate_f5 | 0.2419408 | 0.4029886 | 0.6003664 | 0.5484422 |
| officer_rate_f6 | 0.6359086 | 0.5053006 | 1.2584759 | 0.2086087 |
| bonus_pay | 0.0000211 | 0.0000098 | 2.1572493 | 0.0313011 |
| officer_rate_f4:bonus_pay | -0.0000288 | 0.0000130 | -2.2189548 | 0.0267864 |
| officer_rate_f5:bonus_pay | -0.0000098 | 0.0000169 | -0.5808773 | 0.5614965 |
| officer_rate_f6:bonus_pay | -0.0000239 | 0.0000225 | -1.0656273 | 0.2869328 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.01366 | 0.0045151 | 1.327109 | 1.493731 | 0.1660622 | 8 | -1294.56 | 2607.121 | 2648.856 | 1329.721 | 755 |
ds ~ satisfaction_rank 1 + billet_current + critical_war
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.8377622 | 0.1180247 | 32.5166107 | 0.0000000 |
| billet_currentNon-Operational/Clinical | -0.2015313 | 0.1975770 | -1.0200137 | 0.3080071 |
| billet_currentOCONUS MTF | -0.4370929 | 0.1425983 | -3.0652035 | 0.0022428 |
| billet_currentCONUS Operational | -0.7312263 | 0.1359483 | -5.3787096 | 0.0000001 |
| billet_currentOCONUS Operational | -1.3975988 | 0.2150434 | -6.4991486 | 0.0000000 |
| billet_currentOther | 0.1509960 | 0.4418948 | 0.3417011 | 0.7326588 |
| critical_warLow Deployer | 0.0112418 | 0.1235363 | 0.0910001 | 0.9275136 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0731976 | 0.0667689 | 1.313958 | 11.38609 | 0 | 7 | -1471.894 | 2959.789 | 2997.955 | 1493.409 | 865 |
ds ~ satisfaction_rank 1 + manning_proportion_cut3 + bonus_pay_cut3
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.4680836 | 0.1647969 | 14.976514 | 0.0000000 |
| manning_proportion_cut3Balanced | 0.3928698 | 0.1259855 | 3.118373 | 0.0018786 |
| manning_proportion_cut3Over | 0.4651373 | 0.1209922 | 3.844358 | 0.0001297 |
| bonus_pay_cut3$20-24k | 1.0172580 | 0.1493703 | 6.810311 | 0.0000000 |
| bonus_pay_cut3$24k+ | 0.8796862 | 0.1546106 | 5.689687 | 0.0000000 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0642006 | 0.0598832 | 1.318796 | 14.87015 | 0 | 5 | -1476.106 | 2964.213 | 2992.838 | 1507.907 | 867 |
No interaction between manning_proportion_cut3 & bonus_pay_cut3Analysis of Variance Table
ds ~ satisfaction_rank 1 + billet_current + critical_war
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 3.8377622 | 0.1180247 | 32.5166107 | 0.0000000 |
| billet_currentNon-Operational/Clinical | -0.2015313 | 0.1975770 | -1.0200137 | 0.3080071 |
| billet_currentOCONUS MTF | -0.4370929 | 0.1425983 | -3.0652035 | 0.0022428 |
| billet_currentCONUS Operational | -0.7312263 | 0.1359483 | -5.3787096 | 0.0000001 |
| billet_currentOCONUS Operational | -1.3975988 | 0.2150434 | -6.4991486 | 0.0000000 |
| billet_currentOther | 0.1509960 | 0.4418948 | 0.3417011 | 0.7326588 |
| critical_warLow Deployer | 0.0112418 | 0.1235363 | 0.0910001 | 0.9275136 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.0731976 | 0.0667689 | 1.313958 | 11.38609 | 0 | 7 | -1471.894 | 2959.789 | 2997.955 | 1493.409 | 865 |
Call: lm(formula = satisfaction_rank ~ 1 + billet_current + officer_rate, data = ds_no_other_or_unknown)
Residuals: Min 1Q Median 3Q Max -3.4636 -0.7754 0.2246 0.9947 2.6016
Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.39905 0.20237 11.855 < 2e-16 billet_currentNon-Operational/Clinical -0.52516 0.19724 -2.663 0.007902 billet_currentOCONUS MTF -0.45834 0.13776 -3.327 0.000915 billet_currentCONUS Operational -0.67101 0.13202 -5.083 4.57e-07 billet_currentOCONUS Operational -1.37705 0.20769 -6.630 5.94e-11 officer_rate 0.34410 0.04627 7.436 2.52e-13
Residual standard error: 1.269 on 853 degrees of freedom (77 observations deleted due to missingness) Multiple R-squared: 0.1299, Adjusted R-squared: 0.1248 F-statistic: 25.46 on 5 and 853 DF, p-value: < 2.2e-16
Call: lm(formula = satisfaction_rank ~ 1 + billet_current + officer_rate + specialty_type, data = ds_no_other_or_unknown)
Residuals: Min 1Q Median 3Q Max -3.5180 -0.8131 0.1883 1.0431 2.8817
Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.74494 0.22167 12.383 < 2e-16 billet_currentNon-Operational/Clinical -0.44492 0.19850 -2.241 0.025258 billet_currentOCONUS MTF -0.40357 0.13807 -2.923 0.003559 billet_currentCONUS Operational -0.39353 0.15497 -2.539 0.011283 billet_currentOCONUS Operational -1.14521 0.21495 -5.328 1.27e-07 officer_rate 0.29551 0.04919 6.008 2.78e-09 specialty_typesurgical -0.26554 0.12029 -2.208 0.027545 specialty_typefamily -0.40943 0.11886 -3.445 0.000600 specialty_typeoperational -0.66349 0.18554 -3.576 0.000369 specialty_typeresident -0.19873 0.23598 -0.842 0.399932
Residual standard error: 1.257 on 849 degrees of freedom (77 observations deleted due to missingness) Multiple R-squared: 0.1506, Adjusted R-squared: 0.1416 F-statistic: 16.73 on 9 and 849 DF, p-value: < 2.2e-16
ds_no_other_or_unknown ~ satisfaction_rank 1 + billet_current + officer_rate + specialty_type
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 2.7449416 | 0.2216697 | 12.3830259 | 0.0000000 |
| billet_currentNon-Operational/Clinical | -0.4449198 | 0.1985010 | -2.2413979 | 0.0252580 |
| billet_currentOCONUS MTF | -0.4035686 | 0.1380693 | -2.9229432 | 0.0035595 |
| billet_currentCONUS Operational | -0.3935332 | 0.1549743 | -2.5393446 | 0.0112834 |
| billet_currentOCONUS Operational | -1.1452059 | 0.2149465 | -5.3278645 | 0.0000001 |
| officer_rate | 0.2955084 | 0.0491857 | 6.0080167 | 0.0000000 |
| specialty_typesurgical | -0.2655425 | 0.1202900 | -2.2075190 | 0.0275446 |
| specialty_typefamily | -0.4094283 | 0.1188588 | -3.4446620 | 0.0005998 |
| specialty_typeoperational | -0.6634897 | 0.1855377 | -3.5760380 | 0.0003686 |
| specialty_typeresident | -0.1987343 | 0.2359798 | -0.8421669 | 0.3999317 |
| r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual |
|---|---|---|---|---|---|---|---|---|---|---|
| 0.1506291 | 0.1416252 | 1.256672 | 16.72926 | 0 | 10 | -1410.092 | 2842.184 | 2894.498 | 1340.762 | 849 |
Analysis of Variance Table
Model 1: satisfaction_rank ~ 1 + billet_current + officer_rate Model 2: satisfaction_rank ~ 1 + billet_current + officer_rate + specialty_type Res.Df RSS Df Sum of Sq F Pr(>F) 1 853 1373.5
2 849 1340.8 4 32.789 5.1907 0.0003911
For the sake of documentation and reproducibility, the current report was rendered in the following environment. Click the line below to expand.
Environment
─ Session info ───────────────────────────────────────────────────────────────────────────────────
setting value
version R version 3.6.2 (2019-12-12)
os Ubuntu 19.10
system x86_64, linux-gnu
ui RStudio
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/Chicago
date 2020-01-24
─ Packages ───────────────────────────────────────────────────────────────────────────────────────
package * version date lib source
assertthat 0.2.1 2019-03-21 [1] CRAN (R 3.6.2)
backports 1.1.5 2019-10-02 [1] CRAN (R 3.6.2)
broom 0.5.3 2019-12-14 [1] CRAN (R 3.6.2)
callr 3.4.0 2019-12-09 [1] CRAN (R 3.6.2)
checkmate 1.9.4 2019-07-04 [1] CRAN (R 3.6.2)
cli 2.0.1 2020-01-08 [1] CRAN (R 3.6.2)
colorspace 1.4-1 2019-03-18 [1] CRAN (R 3.6.2)
config 0.3 2018-03-27 [1] CRAN (R 3.6.2)
corrplot 0.84 2017-10-16 [1] CRAN (R 3.6.2)
crayon 1.3.4 2017-09-16 [1] CRAN (R 3.6.2)
DBI 1.1.0 2019-12-15 [1] CRAN (R 3.6.2)
desc 1.2.0 2018-05-01 [1] CRAN (R 3.6.2)
devtools 2.2.1 2019-09-24 [1] CRAN (R 3.6.2)
digest 0.6.23 2019-11-23 [1] CRAN (R 3.6.2)
dplyr 0.8.3 2019-07-04 [1] CRAN (R 3.6.2)
ellipsis 0.3.0 2019-09-20 [1] CRAN (R 3.6.2)
evaluate 0.14 2019-05-28 [1] CRAN (R 3.6.2)
fansi 0.4.1 2020-01-08 [1] CRAN (R 3.6.2)
farver 2.0.3 2020-01-16 [1] CRAN (R 3.6.2)
fs 1.3.1 2019-05-06 [1] CRAN (R 3.6.2)
generics 0.0.2 2018-11-29 [1] CRAN (R 3.6.2)
ggplot2 * 3.2.1 2019-08-10 [1] CRAN (R 3.6.2)
glue 1.3.1 2019-03-12 [1] CRAN (R 3.6.2)
gtable 0.3.0 2019-03-25 [1] CRAN (R 3.6.2)
highr 0.8 2019-03-20 [1] CRAN (R 3.6.2)
hms 0.5.3 2020-01-08 [1] CRAN (R 3.6.2)
htmltools 0.4.0 2019-10-04 [1] CRAN (R 3.6.2)
httr 1.4.1 2019-08-05 [1] CRAN (R 3.6.2)
import 1.1.0 2015-06-22 [1] CRAN (R 3.6.2)
kableExtra 1.1.0 2019-03-16 [1] CRAN (R 3.6.2)
knitr * 1.27 2020-01-16 [1] CRAN (R 3.6.2)
labeling 0.3 2014-08-23 [1] CRAN (R 3.6.2)
lattice 0.20-38 2018-11-04 [4] CRAN (R 3.6.1)
lazyeval 0.2.2 2019-03-15 [1] CRAN (R 3.6.2)
lifecycle 0.1.0 2019-08-01 [1] CRAN (R 3.6.2)
magrittr * 1.5 2014-11-22 [1] CRAN (R 3.6.2)
markdown 1.1 2019-08-07 [1] CRAN (R 3.6.2)
Matrix * 1.2-18 2019-11-27 [4] CRAN (R 3.6.1)
memoise 1.1.0 2017-04-21 [1] CRAN (R 3.6.2)
mitools 2.4 2019-04-26 [1] CRAN (R 3.6.2)
munsell 0.5.0 2018-06-12 [1] CRAN (R 3.6.2)
nlme 3.1-143 2019-12-10 [1] CRAN (R 3.6.2)
OuhscMunge 0.1.9.9012 2020-01-13 [1] local
packrat 0.5.0 2018-11-14 [1] CRAN (R 3.6.2)
pillar 1.4.3 2019-12-20 [1] CRAN (R 3.6.2)
pkgbuild 1.0.6 2019-10-09 [1] CRAN (R 3.6.2)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 3.6.2)
pkgload 1.0.2 2018-10-29 [1] CRAN (R 3.6.2)
prettyunits 1.1.0 2020-01-09 [1] CRAN (R 3.6.2)
processx 3.4.1 2019-07-18 [1] CRAN (R 3.6.2)
ps 1.3.0 2018-12-21 [1] CRAN (R 3.6.2)
purrr 0.3.3 2019-10-18 [1] CRAN (R 3.6.2)
R6 2.4.1 2019-11-12 [1] CRAN (R 3.6.2)
Rcpp 1.0.3 2019-11-08 [1] CRAN (R 3.6.2)
readr 1.3.1 2018-12-21 [1] CRAN (R 3.6.2)
remotes 2.1.0 2019-06-24 [1] CRAN (R 3.6.2)
rlang 0.4.2 2019-11-23 [1] CRAN (R 3.6.2)
rmarkdown 2.1 2020-01-20 [1] CRAN (R 3.6.2)
rprojroot 1.3-2 2018-01-03 [1] CRAN (R 3.6.2)
rsconnect 0.8.16 2019-12-13 [1] CRAN (R 3.6.2)
rstudioapi 0.10 2019-03-19 [1] CRAN (R 3.6.2)
rvest 0.3.5 2019-11-08 [1] CRAN (R 3.6.2)
scales 1.1.0 2019-11-18 [1] CRAN (R 3.6.2)
sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.6.2)
stringi 1.4.5 2020-01-11 [1] CRAN (R 3.6.2)
stringr 1.4.0 2019-02-10 [1] CRAN (R 3.6.2)
survey * 3.37 2020-01-21 [1] CRAN (R 3.6.2)
survival * 3.1-8 2019-12-03 [1] CRAN (R 3.6.2)
TabularManifest 0.1-16.9003 2019-12-15 [1] Github (Melinae/TabularManifest@4cbc21c)
testit 0.11.1 2020-01-09 [1] Github (yihui/testit@c1c19f8)
testthat 2.3.1 2019-12-01 [1] CRAN (R 3.6.2)
tibble 2.1.3 2019-06-06 [1] CRAN (R 3.6.2)
tidyr 1.0.0 2019-09-11 [1] CRAN (R 3.6.2)
tidyselect 0.2.5 2018-10-11 [1] CRAN (R 3.6.2)
usethis 1.5.1 2019-07-04 [1] CRAN (R 3.6.2)
utf8 1.1.4 2018-05-24 [1] CRAN (R 3.6.2)
vctrs 0.2.1 2019-12-17 [1] CRAN (R 3.6.2)
viridisLite 0.3.0 2018-02-01 [1] CRAN (R 3.6.2)
webshot 0.5.2 2019-11-22 [1] CRAN (R 3.6.2)
withr 2.1.2 2018-03-15 [1] CRAN (R 3.6.2)
xfun 0.12 2020-01-13 [1] CRAN (R 3.6.2)
xml2 1.2.2 2019-08-09 [1] CRAN (R 3.6.2)
yaml 2.2.0 2018-07-25 [1] CRAN (R 3.6.2)
zeallot 0.1.0 2018-01-28 [1] CRAN (R 3.6.2)
[1] /home/wibeasley/R/x86_64-pc-linux-gnu-library/3.6
[2] /usr/local/lib/R/site-library
[3] /usr/lib/R/site-library
[4] /usr/lib/R/library
Report rendered by wibeasley at 2020-01-24, 23:19 -0600 in 27 seconds.